The microstructure of cement paste determines the overall performance of concrete and therefore obtaining the microstructure is an essential step in concrete studies. Traditional methods to obtain the microstructure, such as scanning electron microscopy (SEM) and X-ray computed tomography (XCT), are time-consuming and expensive. Herein we propose using Denoising Diffusion Probabilistic Models (DDPM) to synthesize realistic microstructures of cement paste. A DDPM with a U-Net architecture is employed to generate high-fidelity microstructure images that closely resemble those derived from SEM. The synthesized images are subjected to comprehensive image analysis, phase segmentation, and micromechanical analysis to validate their accuracy. Findings demonstrate that DDPM-generated microstructures not only visually match the original microstructures but also exhibit similar greyscale statistics, phase assemblage, phase connectivity, and micromechanical properties. This approach offers a cost-effective and efficient alternative for generating microstructure data, facilitating advanced multiscale computational studies of cement paste properties.

This review provides a comprehensive analysis of the fatigue behaviour of cementitious materials, focusing on the characterisation, modelling, and mechanisms underlying their fatigue properties. It begins with a detailed exploration of how material composition and loading conditions influence fatigue performance, along with the underlying mechanisms that govern fatigue fracture processes. The review also synthesises current advanced experimental methods for characterising fatigue damage evolution, highlighting significant achievements and methodological innovations. It also examines theoretical approaches, summarising prominent models and emerging theories that deepen the understanding of fatigue behaviour in cementitious materials. Meanwhile, the paper reviews computational techniques based on fracture mechanics, damage mechanics, statistical analysis, machine learning algorithms and multiscale modelling, assessing their potential to improve the accuracy of fatigue predictions. Lastly, the review identifies crucial research gaps and outlines future directions, particularly in understanding fatigue-related coupling mechanisms to enhance the fatigue resistance and durability of cementitious materials.

Alkali-activated materials (AAMs) are a class of potentially eco-friendly construction materials that can contribute to reduce the environmental impact of the construction sector by offering an alternative to Portland cement (PC). With the rapid development of both computational capabilities and theoretical insights into alkali-activation reaction processes, there has been a surge in research activities worldwide, leading to a growing demand for computational methods that can describe different characteristics of AAMs. This review summarizes the collective efforts made in the past two decades on this topic, and highlights the most relevant results and advances in the aspects of atomistic simulation, thermodynamic modeling, microstructure/−based simulation, and multi-scale modeling. The gaps and challenges in current numerical research on AAMs are pointed out and discussed in comparison with PC-based materials. This review aims to provide a critical overview of the state-of-the-art in modeling and simulating AAMs, while also outlining potential avenues for future development.

Engineered cementitious composite (ECC) is widely employed in engineering due to its high toughness and ductility. The Interfacial Transition Zone (ITZ) between the fibers and the matrix plays a vital role in influencing the strength and durability of ECC. This study introduces a numerical method to simulate fiber pull-out behaviors, specifically the fiber debonding and slipping. A birth-death method is proposed to account for the mechanical transition from fiber debonding stage to slipping stage. The contributions of various phases in the ITZ are explicitly considered. Furthermore, nanoindentation tests and Backscattered Electron (BSE) imaging were conducted to determine the microstructures of ITZ and local mechanical properties of each phase within the ITZ. A series of fiber pullout experiments with polyvinyl alcohol (PVA) fibers were conducted to calibrate and validate the model. Subsequently, the validated model was employed to explore the influence of w/c ratios, fiber orientations and bonding properties on the interfacial behavior. The microstructure-informed model proposed herein effectively predicts fiber pull-out behavior, facilitating a thorough exploration of fracture mechanisms throughout the pull-out process, and serves as the basis for multiscale modeling of ECC.

Tailoring lattice structures is a commonly used method to develop lattice materials with desired mechanical properties. However, for cementitious lattice materials, besides the macroscopic lattice structure, the multi-phase microstructure of cement paste may have substantial impact on the mechanical responses. Therefore, this work proposes a multi-scale numerical modelling method to simulate the deformation and fracture behavior of cementitious lattice materials, such that the influence of cement paste microstructure can be properly captured. On the microscale, the load–displacement response of cement paste is numerically simulated then experimentally validated. In order to rationally investigate the role of cement paste microstructure, the obtained load–displacement response was then formulated to several types of model inputs reflecting different degree of brittleness. These inputs were then used for simulating the mechanical response of macroscale cementitious lattices. By comparing the simulation to experiment, multi-linear behavior (ML) was found to an appropriate method to include the realistic pre-critical cracking and post-peak softening of cement paste in the model. Compared to ideally brittle behavior, using ML as input, the discrepancy between simulated and experimentally tested fracture energy decreases from 37.4% to 12.4%. In addition, the influence of lattice structure on the strength of cementitious lattices was also accurately captured by the proposed model. Randomized cementitious lattice has 21.6% (22.0% from simulation) lower strength than regular lattice. Moreover, the influence of fracture criterion of the proposed model is discussed and elaborated. Owning to the high simulation accuracy, the proposed multi-scale method in this work could be helpful for tailoring the fracture cementitious lattice materials for future studies.

This paper reports the carbonation characteristics of a cement-slag system exposed to accelerated carbonation testing, and its improved carbonation resistance with the increasing MgO content in blast furnace slag, in which hydrotalcite-like phase plays a key role. Our research showed that the hydrotalcite-like phase started to carbonate upon contacting with the carbonate ions and bound more than 15 wt% CO₂⁻³ in the mildly carbonated and transition areas. This value was positively associated with the magnesia content of slag. Additionally, the proportion shared by hydrotalcite-like phase decreased in the fully carbonated area, and more CO₂ was fixed in the form of calcium carbonate. Consistent with the thermodynamic modelling, the ratio of CO₂ bound in carbonated hydrotalcite-like phase to the total CaCO₃ continued to decrease as the CO₂ ingress progressed. On the other hand, the reaction between hydrotalcite-like phase and CO₂ was found to be volumetrically stable due to binding CO₂ in the interlayer space, and Mg was still distributed within the original slag grain region. Mg/Al atomic ratio of hydrotalcite-like phase remained nearly the same before and after carbonation. Results of this study quantitatively emphasized the favorable effect of hydrotalcite-like phase to improve the carbonation resistance of slag-rich cementitious systems.

Stress evolution of restrained concrete is a significant direct index in early-age cracking (EAC) analysis of concrete. This study presents experiments and numerical modelling of the early-age stress evolution of Ground granulated blast furnace slag (GGBFS) concrete, considering the development of autogenous deformation and creep. Temperature Stress Testing Machine (TSTM) tests were conducted to obtain the autogenous deformation and stress evolution of restrained GGBFS concrete. By a self-defined material subroutine based on the Rate-type creep law, the FEM model for simulating the stress evolution in TSTM tests was established. By characterizing the creep compliance function with a 13-units continuous Kelvin chain, forward modelling was firstly conducted to predict the stress development. Then inverse modelling was conducted by Bayesian Optimization to efficiently modify the arbitrary assumption of the codes on the aging creep. The major findings of this study are as follows: 1) the high autogenous expansion of GGBFS induces compressive stress at first hours, but its value is low because of high relaxation and low elastic modulus; 2) The codes highly underestimated the early-age creep of GGBFS concrete. They performed well in prediction of stress after 200 h, but showed significant gaps in predictions of early-age stress evolution; 3) The proposed inverse modelling method with Bayesian Optimization can efficiently adjusted the aging terms which produced best modelling results. The adjusted creep compliance function of GGBFS showed a much faster aging speed at early ages than the one proposed by original codes.

This study aims to provide an efficient alternative for predicting creep modulus of cement paste based on Deep Convolutional Neural Network (DCNN). First, a microscale lattice model for short-term creep is adopted to build a database that contains 18,920 samples. Then, 3 DCNNs with different consecutive convolutional layers are built to learn from the database. Finally, the performance of DCNNs is tested on unseen testing samples. The results show that the DCNNs can achieve high accuracy in the testing set, with the R² all higher than 0.96. The distribution of creep modulus predicted by the DCNNs coincides with that of the original data. Furthermore, through analyzing the feature maps, it is found that the DCNNs can correctly capture the local importance of different microstructural phases. The DCNN allows therefore prediction of the creep modulus based on microstructural input, which saves computational resources of segmentation procedure and multiple incremental FEM calculations.

This study aims to provide an efficient and accurate machine learning (ML) approach for predicting the creep behavior of concrete. Three ensemble machine learning (EML) models are selected in this study: Random Forest (RF), Extreme Gradient Boosting Machine (XGBoost) and Light Gradient Boosting Machine (LGBM). Firstly, the creep data in Northwestern University (NU) database is preprocessed by a prebuilt XGBoost model and then split into a training set and a testing set. Then, by Bayesian Optimization and 5-fold cross validation, the 3 EML models are tuned to achieve high accuracy (R² = 0.953, 0.947 and 0.946 for LGBM, XGBoost and RF, respectively). In the testing set, the EML models show significantly higher accuracy than the equation proposed by the fib Model Code 2010 (R² = 0.377). Finally, the SHapley Additive exPlanations (SHAP), based on the cooperative game theories, are calculated to interpretate the predictions of the EML model. Five most influential parameters for concrete creep compliance are identified by the SHAP values of EML models as follows: time since loading, compressive strength, age when loads are applied, relative humidity during the test and temperature during the test. The patterns captured by the three EML models are consistent with theoretical understanding of factors that influence concrete creep, which proves that the proposed EML models show reasonable predictions.

This paper aims to provide a systematical review of the available printing strategies, sustainable cementitious materials and characterization methods for extrusion-based 3D concrete printing (3DCP). The printing strategies, consisting of printing setup, process, and material requirements, were summarized initially. In the material aspect, the high ordinary Portland cement (OPC) content in most printable mixtures is a major issue that impedes the sustainability of 3DCP. This can be resolved by partially substituting OPC with supplementary cementitious materials (SCMs). In this review, the effect of different SCMs on fresh-state behaviors and 3D printing of cementitious materials was comprehensively discussed. Finally, a series of test methods for quantitively characterizing fresh properties, 3D printability and interlayer behaviors were summarized and reviewed.

In this study, the flexural strength and fatigue properties of interfacial transition zone (ITZ) were experimentally investigated at the micrometre length scale. The hardened cement paste cantilevers (150 × 150 × 750 μm³) attached to a quartzite aggregate surface were prepared and tested under the monotonic and cyclic load using a nanoindenter. The measured flexural strength of the ITZ (10.49–14.15 MPa) is found to be one order of magnitude higher than the macroscopic strength of ITZ reported in literature. On the other hand, the fatigue strength of the ITZ is lower than that of bulk cement paste at same length scale, measured previously by the authors. The microscopic mechanical interlocking and the electrostatic interaction between aggregate surface and hydration products are thought to contribute to the bond strength of ITZ. This study provides an experimental basis for the development of multiscale analysis of concrete subjected to both static and fatigue loading.

Early-age stress (EAS) is an important index for evaluating the early-age cracking risk of concrete. This paper encompasses a thermo-chemo-mechanical (TCM) model and active ensemble learning (AEL) for predicting the EAS evolution. The TCM model provides the data for the AEL model. First, based on Fourier's law, Arrhenius’ equation, and rate-type creep law, a TCM model is built to simulate the heat transfer, cement hydration, and viscoelasticity, which together determine the EAS evolution. Then, a material model composed of an eXtreme Gradient Boosting model and adjusted Model Code 2010 is built to allow for parametric study and database construction. Finally, an AEL framework is built, which incorporates principal component analysis (PCA), Gaussian process, and light gradient boosting machine (LGBM). This study resulted in the following findings: (1) The dimensionality of the 672-by-1 EAS vector can be effectively reduced by PCA, and the first principal component (PC) is a global index representing the magnitude of the EAS; (2) the mechanical field of the TCM model is validated by testing data. Correlation analysis on the first PC quantifies the influence of various input parameters of the TCM model, which is in accordance with common understandings of the EAS evolution process. (3) The AEL and one-shot ensemble learning (OSEL) both achieve high prediction performance in the testing set, whose R² reaches 0.961 and 0.948, respectively. Thanks to the uncertainty-based query procedure, comparing with OSEL, AEL shows advantages in prediction performance over the whole training history. (4) AEL can significantly reduce the number of samples required for training, which can be a major improvement in efficiency considering the computational cost of the TCM model.

This study presents an experimental investigation of the rate-dependent mechanical properties of cement paste at the microscale. With the use of a nanoindenter, micro-cantilever beams with the size of 300 μm × 300 μm × 1650 μm were loaded at five different strain rates from around 10⁻⁶/s to 10⁻²/s until failure. It is found that with increasing strain rate, the stress-strain curves show less and delayed pre-peak nonlinearity. Both the flexural strength and the elastic modulus of beams increase with increasing strain rate, while the strain at peak stress exhibits an opposite trend. Examination of the fracture surface indicates that with increasing strain rate the possibility of a crack to pass through stronger components of the hydration products is increased. The experimental observations and possible mechanisms leading to changes in mechanical responses are discussed. It is suggested that at least two micromechanical processes, namely creep and Stéfan effect, are mainly responsible for the rate-dependent behaviour of cement paste within the investigated strain rate range and their dominances seem to vary with the strain rate. At lower strain rate, the strain rate sensitivity of cement paste is thought to be dominated by the creep effect, while at higher strain rate the Stéfan effect appears to be the governing factor.

In this work, the lattice model is applied to study the printing process and quantify the buildability (i.e., the maximum height that can be printed) for 3D concrete printing (3DCP). The model simulates structural failure by incorporating an element birth technique, time-dependent stiffness and strength, printing velocity, non-uniform gravitational load, localized damage, and spatial variation of the printed object. The model can reproduce the plastic collapse failure modes reported in the literature. In this research, three main contributions for 3DCP modeling work can be found. A new failure criterion is proposed and adopted to improve the estimation of critical printing height; the element birth technique is utilized to mimic the continuous printing process and study the impact of non-uniform gravitational load; variability of a printed structure is modeled through the inclusion of disorder during mesh generation and Gaussian distributions of material properties. Using this model, parametric analyses on non-uniform gravitational load and material variation are conducted to assess their impact on the failure–deformation response and the critical printing height. Finally, the model is validated by comparison with two 3D printing experiments from the literature. The proposed lattice model can reproduce the correct failure-deformation modes of two types of structures commonly used for buildability quantification: A 3D-printed hollow cylinder and a square wall layout. Lattice modeling of the square structure yields a relative difference of around 10% with the experimental printing height. For the cylinder structure, the predicted radial deformation and corresponding height show good agreement with the experimental data; the model yields a 41.38% overprediction of the total number of printing layers, compared with the experimental data. Possible reasons for the quantitative discrepancy are discussed.

This paper presents a method to numerically investigate the microstructural effect on the creep behavior of cement paste at the microscale. The lattice fracture model is extended to consider local time-dependent deformations of calcium-silicate-hydrate phases in the cement paste by imposing local forces. The term “experimentally informed model” is used herein as the heterogeneous microstructures of hardened cement pastes were obtained by using the X-ray computed microtomography and directly implemented into the model. The mechanical and creep properties of different constituents at the resolution of 5 µm were inversely identified from the fracture and creep bending tests on cementitious microcantilever beams at the microscale. The model is then validated through the comparison with the testing results of cement pastes with different w/c ratios and microstructures. It is found that the developed model can successfully reproduce experimentally observed behaviors and be applied to explain the experimental results in detail. With the method presented in this paper, the relationship between the volume fractions of different components and the global creep behavior of cement paste can be established. The validation of the model performed at the microscale forms a basis for the multiscale analysis of concrete creep.

In this study, a numerical model using a 2D lattice network is developed to investigate the fatigue behaviour of cement paste at the microscale. Images of 2D microstructures of cement pastes obtained from XCT tests are used as inputs and mapped to the lattice model. Different local mechanical and fatigue properties are assigned to different phases of the cement paste. A cyclic constitutive law is proposed for considering the fatigue damage evolution. Fatigue experiments performed at the same length scale are used to calibrate and validate the model. The proposed model can reproduce well the flexural fatigue experimental results, in terms of S-N curve, stiffness degradation and residual deformation. The validated model is then used to predict the uniaxial tensile fatigue fracture of cement paste. The effects of microstructure and stress level on the fatigue fracture are studied using the proposed model. This model forms a basis for the multiscale analysis of concrete fatigue.

Cementitious materials are widely used in construction. For their low ductility, they typically need to be reinforced by steel rebars, which cause potential corrosion problems. Polymeric reinforcement, which does not have corrosion problems, has been used to replace steel rebars. However, a relatively high reinforcing ratio is usually required for the cementitious composites reinforced by conventional polymeric reinforcement. Owing to the customizability of 3D printing technology, polymeric reinforcement with a functionally graded structure is able to be manufactured, which significantly reduces the reinforcing ratio of the reinforced cementitious composites meanwhile improves their mechanical properties. In this present study, 3D printed polymeric octet lattice structures were used as reinforcement to develop cementitious composites with enhanced ductility. Four-point bending experiments were performed on the plain mortar, and the reinforced specimens and a finite element model was used to simulate the experiments numerically. A good agreement between experiments and simulations was found: the reinforced specimens have a significantly increased flexural ductility comparing to plain mortar. Composites reinforced by vertically functionally graded lattice structures have a significantly lower reinforcing ratio while exhibiting obviously higher normalized ductility. In addition, the fracture behavior of the reinforced cementitious composites was evaluated using a fracture energy based analytical model. The analysis shows that, from the perspective of fracture energy release, the steady state cracking criteria were not satisfied by the cementitious composites developed in this study so that multiple cracking and strain hardening behavior was not obtained. However, according to numerical predictions, increasing strength of the printed reinforcement material by 40% would allow these behaviors to be potentially achieved. This work shows that additive manufacturing has great potential for developing reinforcement for cementitious materials to reduce the reinforcing ratio and enhance ductility.

This study presents an experimental investigation of fatigue properties of cement paste at the microscale. A strong size dependence is found for the flexural fatigue life of the cement paste specimen. Microscopic observations on the fractured surfaces suggest that there is a higher density of nano-scale cracks generated during the fatigue loading compared to the static fracture. However, the fatigue damage evolution is found to be very slow and small even under high stress levels. The development of residual deformation for cement paste can be explained by the combined effects of viscoelastic deformation and fatigue cracking growth.